Digital signal processing for the detection of hidden objects using an FMCW radar.
Abstract
This thesis deals with the detection of hidden objects using a short-range
frequency-modulated continuous wave (FMCW) radar. The detection is carried out
by examining the estimated Power Spectral Density (PSD) functions of sampled
returns, the peaks of which theoretically correspond to the reflecting surfaces of
hidden objects.
Fourier and non-Fourier PSD estimation algorithms are applied to the radar
returns to extract information on the hidden surfaces. The Fourier methods used are
Direct, Blackman-Tukey, Bartlett, and Smoothed Periodograms. The different PSDs
are compared, and the validity of each PSD is then discussed. The study is new for
this type of radar and the results are used as references for other PSD estimations.
Non-Fourier methods offer many advantages. Firstly the Autoregressive
Process (AR) is used for this particular application. As well as PSDs the noise
spectra are also produced to show the performance of the chosen models. An
alternative approach to the conventional forward-backward residuals ( e. g. Burg's
method) or autocorrelation and covariance methods ( as those used in speech
analysis ) is introduced in this thesis. The stability and good resolution of the PSDs
is obtained by a better estimation of the autocovariance coefficients (ACF) from the
data available : averaging two p-shifted ACF calculated by covariance method. Once
the covariance coefficients are found, the Levinson-Durbin recursive algorithm is
used to get the model parameters and the PSDs.
Two other non-conventional methods are also attempted to show the image of
hidden objects. They are Pisarenko Harmonic Decomposition method and Prony
energy spectrum density estimation.
In addition to the one-dimensional processing stated above, this thesis extends
it to two-dimensional cases, which give more information on the shape of hidden
objects.
Authors
Liau, Teh-FuCollections
- Theses [3303]